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"""Introduction to the CoNLL-2000 Shared Task: Chunking""" |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{tksbuchholz2000conll, |
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author = "Tjong Kim Sang, Erik F. and Sabine Buchholz", |
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title = "Introduction to the CoNLL-2000 Shared Task: Chunking", |
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editor = "Claire Cardie and Walter Daelemans and Claire |
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Nedellec and Tjong Kim Sang, Erik", |
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booktitle = "Proceedings of CoNLL-2000 and LLL-2000", |
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publisher = "Lisbon, Portugal", |
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pages = "127--132", |
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year = "2000" |
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} |
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""" |
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_DESCRIPTION = """\ |
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Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence |
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He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows: |
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[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ] |
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[PP in ] [NP September ] . |
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Text chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test |
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data for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ) |
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as the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as |
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test data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by |
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Sabine Buchholz from Tilburg University, The Netherlands. |
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""" |
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_URL = "https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/" |
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_TRAINING_FILE = "train.txt" |
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_TEST_FILE = "test.txt" |
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class Conll2000(datasets.GeneratorBasedBuilder): |
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"""Conll2000 dataset.""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"pos_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"''", |
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"#", |
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"$", |
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"(", |
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")", |
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",", |
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".", |
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":", |
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"``", |
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"CC", |
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"CD", |
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"DT", |
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"EX", |
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"FW", |
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"IN", |
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"JJ", |
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"JJR", |
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"JJS", |
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"MD", |
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"NN", |
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"NNP", |
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"NNPS", |
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"NNS", |
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"PDT", |
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"POS", |
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"PRP", |
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"PRP$", |
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"RB", |
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"RBR", |
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"RBS", |
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"RP", |
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"SYM", |
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"TO", |
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"UH", |
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"VB", |
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"VBD", |
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"VBG", |
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"VBN", |
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"VBP", |
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"VBZ", |
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"WDT", |
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"WP", |
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"WP$", |
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"WRB", |
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] |
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) |
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), |
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"chunk_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-ADJP", |
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"I-ADJP", |
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"B-ADVP", |
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"I-ADVP", |
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"B-CONJP", |
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"I-CONJP", |
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"B-INTJ", |
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"I-INTJ", |
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"B-LST", |
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"I-LST", |
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"B-NP", |
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"I-NP", |
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"B-PP", |
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"I-PP", |
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"B-PRT", |
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"I-PRT", |
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"B-SBAR", |
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"I-SBAR", |
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"B-UCP", |
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"I-UCP", |
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"B-VP", |
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"I-VP", |
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] |
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) |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://www.clips.uantwerpen.be/conll2000/chunking/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_URL}{_TRAINING_FILE}", |
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"test": f"{_URL}{_TEST_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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guid = 0 |
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tokens = [] |
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pos_tags = [] |
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chunk_tags = [] |
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for line in f: |
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if line == "" or line == "\n": |
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if tokens: |
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yield guid, {"id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags} |
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guid += 1 |
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tokens = [] |
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pos_tags = [] |
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chunk_tags = [] |
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else: |
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splits = line.split(" ") |
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tokens.append(splits[0]) |
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pos_tags.append(splits[1]) |
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chunk_tags.append(splits[2].rstrip()) |
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yield guid, {"id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags} |
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